Introduction
Autism treatment remains a complex and debated topic, yet Applied Behavior Analysis (ABA) stands out as the scientifically backed gold standard for reducing harmful symptoms and teaching essential skills. Despite its effectiveness, the demand far exceeds the supply, leaving hundreds of thousands of children on waitlists across the United States.
Enter the era of Artificial Intelligence (AI), which is reshaping various industries, including Applied Behavior Analysis. As ABA and AI integration occurs, Board Certified Behavior Analysts (BCBAs) will enjoy highly capable, skill-augmenting, AI co-pilots just like programmers and investment bankers already use. Even teachers and schools are jumping on board!
But how will this tech assist BCBAs, who do such highly technical and complex work? Let’s explore that possibility!
AI is not just automating tasks; it is enhancing or surpassing human capabilities (World Economic Forum). The following graph illustrates how AI exceeds human baseline performance in various domains, given thorough training data. Astute observers will also note that the time for AI to match human performance is also decreasing on a nearly exponential scale.
We can reason straightforwardly, then: if AI can perform as well as humans on verbal reasoning, visual processing, and math — they will soon outperform human BCBAs on tasks requiring those skills, and complete them in a fraction of the time that it takes a human.
What tasks do BCBAs do almost everyday that fall within those domains?
- Writing treatment plans, behavior plans, and payer reports
- Creating graphs and performing analyses
- Interpreting graphs and data
Limitations and Solutions
You may be thinking, “hold on, GPT-4 or Opus can’t do that yet,” and you’re right. Current large language models (LLMs) certainly have some challenges — they can hallucinate false information, their context windows are too small for ABA datasets (i.e. can’t ‘think’ about all necessary info at once), and they don’t understand Applied Behavior Analysis because their training datasets are too broad and uncalibrated.
However, it is important to realize that these issues are symptoms of the new technology’s growing pains, rather than insurmountable obstacles. Computer scientists have already devised a way to store and retrieve client-specific and ABA-domain specific data, eliminating hallucinations. The context window may be small, but no matter — dozens of separate instances (“agents”) of a language model are able to all collaborate on a large task (see https://www.crewai.com/) to provide adequate redundancy and speed. Last but not least, models are able to be fine-tuned on ABA-based training data to perform extremely well on ABA-related tasks.
As you can see all of these technologies are not only possible, but solutions are already developed!
So, will this replace BCBAs? Not without these AI also being able to collect their own data, make their own decisions, and automatically incorporate feedback. However, in the mean time, it will absolutely be responsible for creating a sea change in the workflow for BCBAs.
AI-Augmented BCBAs: Enhancing Efficiency and Reducing Burnout
Just glancing at Reddit’s Behavior Analysis threads over the last couple of years reveal some common themes:
If the theme isn’t obvious, BCBAs are largely burnt out (70%+!). The never-ending and repetitive report writing, remembering all of the separate payer rules, and RBT turnover are largely responsible. Not to mention the sheer amount of data that can be analyzed is larger than any human could consider, truthfully, and for some this is also source of overwhelm or analysis paralysis.
Let’s critically evaluate a potential solution by examining the pros and cons of BCBAs (or BCaBAs) utilizing AI Copilots for repetitive tasks::
Pros:
- No longer write repetitive report content, finish 5–10 cases worth of treatment planning and report writing in 1–2 hours.
- More time for working with RBTs, parents, teachers, etc.
- Higher degree of client treatment plan individualization
- Serving more clients per BCBA, reducing waitlists
- Higher rates of compliance with regulations and payer requirements
- Improving average treatment plan quality, narrowing the experience gap between new BCBAs and veterans
- Lower rates of burnout (at a whopping 72% for ABA currently)
- Increased accessibility for clients, parents, and other stakeholders in the treatment planning process
Cons:
- Additional costs incurred? (BCBAs will replace lost writing/analysis “office BCBA’ hours with other necessary billable activities or with additional clients)
- BCBA loss of skills? (Some old skills, sure. Along with the rest of the workforce that adopts AI. What’s important is prioritizing the correct skills to keep and upgrade)
- Privacy and regulatory concerns? (All tools developed and released for BCBA-use must of course be HIPAA compliant and abide by FDA guidelines)
Looking ahead, what additional features could AI offer to BCBAs?
- Use voice prompts alone for writing treatment documents and making program changes — complete all necessary BCBA tasks on your phone
- Receive treatment plan suggestions for goals/objectives never previously considered because AI can utilize vast research databases of intervention goals vs. client outcomes
- Auto-alerts for time sensitive metrics such as under-performance on specific programs, program/target mastery, or behavior intervention needs.
- Enjoy higher efficacy behavior intervention strategies using AI-automated data recording/processing and uncover new insights into client behavior.
New insights into behavior?
Yes — take a look at what a machine learning model recently found was the best predictor (highest “Feature Value”) for the necessary intensity for ABA therapy:
This is an isolated example, but the point stands — this predictive ‘feature’ existed in that dataset, and humans would possibly never find it on their own. I’m betting many analogous examples are present and waiting to be uncovered!
Managing the Great AI Integration
If this sounds like a lot to take in, don’t worry — it’s only the beginning ! If you’re reading this in 2024, you’re still early — large scale investment and development has only begun!
The OECD and other organizations stress that highly skilled jobs are at the highest risk for AI-integration related disruption, hence the importance of preparing for and adapting to AI-induced changes.
Due to the extraordinarily rapid pace of this change, experts in the Harvard Business Review are recommending that employers begin to budget for AI upskilling — boldly proclaiming it as the “responsibility of every leader and manager.”
For BCBAs and similar professionals, this means adapting to use AI as a tool that complements their expertise, allowing them to successfully manage larger caseloads, create better outcomes, and/or focus on other aspects of their work that significantly impact those who we care about the most — the clients.
What do you think? Leave a comment about it, let’s shape the future of AI and ABA to be the brightest one possible!
